scholarly journals Credit-worthiness Prediction in Energy-Saving Finance using Machine Learning Model

2021 ◽  
Vol 8 (2) ◽  
pp. 88-92
Author(s):  
Eka Sudarmaji ◽  
Noer Azam Achsani ◽  
Yandra Arkeman ◽  
Idqan Fahmi
Keyword(s):  
Machine Learning ◽  
Energy Saving ◽  
Life Cycle Cost ◽  
Technological Factor ◽  
Business Owners ◽  
Life Cycle Cost Analysis ◽  
Scoring Model ◽  
Machine Learning Model ◽  
Credit Worthiness

Companies can form their own "ESCO model" with their capitals. New opportunities that Energy Saving Company (ESCO) can do was to offer PSS business model in the form of Energy Saving Agreement (ESA) or Energy Saving Performance Contract (ESPC), which was known as "saving back arrangement financing." ESCO contracts could free business owners from new upfront investment. Unfortunately, customer's creditworthiness was becoming more crucial for ESCO. Machine learning was used to predict the creditworthiness of clients in ESCO financing processes. This research aimed to develop a scoring model to leverage a machine learning and life cycle cost analysis (LCCA) to evaluate alternative financing for Energy Saving in Indonesia. Research from the case studies leads to a clearer understanding of the factors that affect all parties' decisions to implement and continue with their ESCO project. Both considerations, technology, and administration emerge from this case study which greatly influenced the participants to adopt the decision and continue with the ESCO project. In contrast, both parties agreed to solve the credit risk constraints on the project. This study indicates that administration influences were more significant than the technological factor in shaping their decisions.

Life cycle cost analysis: A case study of hydrogen energy application on the Orkney Islands

2019 ◽  
Vol 44 (19) ◽  
pp. 9517-9528 ◽  
Author(s):  
Guangling Zhao ◽  
Eva Ravn Nielsen ◽  
Enrique Troncoso ◽  
Kris Hyde ◽  
Jesús Simón Romeo ◽  
...  
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Hydrogen Energy ◽  
Life Cycle Cost Analysis ◽  
Orkney Islands ◽  
Energy Application

Implementing the Life Cycle Cost Analysis in a Building Design

2017 ◽  
Vol 21 ◽  
pp. 581-586 ◽  
Author(s):  
Raluca Dania Todor ◽  
Mircea Horne Horneț ◽  
Nicolae Fani Iordan
Keyword(s):  
Life Cycle ◽  
Cost Analysis ◽  
Life Cycle Cost ◽  
Evaluation Process ◽  
Building Design ◽  
Laboratory Research ◽  
Life Cycle Cost Analysis ◽  
Natural Lighting ◽  
Broad Overview

In the context of increasing concerns for sustainable development new comprehensive methods are developed by builders and architects in order to reduce the environmental impact of buildings. Life Cycle Cost Analysis (LCCA) is one of these methods, perhaps the most functional one for the evaluation process. Using this LCCA contributes to the integration of the design process and helps identify opportunities for energy efficiency, such as appropriate zoning, natural lighting and design optimization of heating, ventilation and air conditioning (HVAC). It also helps in finding the best solutions for reducing overall costs. LCCA is very little known in Romania and quasi unused practice for building design and for this reason the present paper contains a broad overview of the methodology and it’s uses highlighting its main advantages and a case study of the building design intended for laboratory research. The analyzed building is one of the 12 identical buildings of Transilvania University Research and Development Institute from Brasov.

scholarly journals Machine Learning Generalisation across Different 3D Architectural Heritage

2020 ◽  
Vol 9 (6) ◽  
pp. 379 ◽  
Author(s):  
Eleonora Grilli ◽  
Fabio Remondino
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
High Complexity ◽  
Architectural Heritage ◽  
Learning Techniques ◽  
Machine Learning Model ◽  
Point Cloud Classification

The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns).

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